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Aerial photo-interpretation using Z/I DMC images for estimation of forest variables

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Interpretation and tree height measurements in aerial photographs using photogrammetric workstations are frequently performed in standwise forest inventory. Images acquired by digital aerial cameras are now replacing the traditional film-based aerial photographs. In this study, digital images from the airborne Z/I DMC system for standwise estimation of stem volume, tree height and tree species composition were investigated at a 1200 ha forest area located in southern Sweden (58°30'N, 13°40'E). The 56 selected stands were dominated by Norway spruce [Picea abies (L.) Karst.] and Scots pine (Pinus sylvestris L.) with stem volume in the range of 30-630 m3 ha-1 (average 300 m3 ha-1) and tree height in the range of 6-28 m (average 20 m). The large-format pansharpened colour infrared images were captured at a flight altitude of 4800 m above ground level corresponding to a pixel size of 0.48 m. The photo-interpretation was conducted by four professional interpreters, independently. In particular, two different base-to-height ratios (i.e. the ratio between the ground distance between image centres at the time of exposure and the flight altitude above ground level) of 0.26 and 0.39 were evaluated, but no significant difference in the estimation accuracy for stem volume and tree height was found. The accuracy for stem volume estimation in terms of relative root mean square error, corrected for systematic errors, was on average 24% (in the range of 17-39%). The corresponding accuracy for tree height estimation was on average 1.4 m (in the range of 0.9-1.6 m). The tree species composition accuracy assessment using a fuzzy set evaluation procedure showed that 95% of the stands were correctly classified. The estimation accuracies are in agreement with previous results using conventional film-based aerial panchromatic photographs.

Keywords: Digital photogrammetry; forest inventory; stand level; stem volume; tree height; tree species composition

Document Type: Research Article


Affiliations: Department of Forest Resource Management, Remote Sensing Laboratory, Swedish University of Agricultural Sciences, Umeå, Sweden

Publication date: January 1, 2007

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